Call centers have evolved over the past decades into highly efficient systems. Introducing a discontinuous technology solution into an efficient call center can adversely impact key performance metrics such as average handle time and customer satisfaction. This can pose a challenge for introducing cognitive technology into a call center, as many traditional products require a training period to adapt the system to the specific use case.
Call center agents rely on collaboration and guidance from seasoned team members (also known as subject matter experts (SME)) to effectively and efficiently serve customers. Prior art cognitive products require a manual training period to adapt the solution to the specific industry domain and to train models against the natural language utterances common to the specific use case. During this training period, the ability of the prior art cognitive systems to return acceptable answers is relatively low. Traditional models require the call center agents to correct the system when an incorrect answer is given, but typically call center agents have little time to perform this function and will discard systems that are overly cumbersome.